In this paper, we have proposed and investigated a number of mixing-related data augmentation methods to facilitate the training of deep learning models for music source separation. Result demonstrates the effectiveness of the proposed augmentation methods in the case of small data. When the training data is big enough, the existing random-mixing based approach [1]} is strong enough. We have also shown that the implemented models outperform the current available best model, Spleeter [2]}, in violin/piano separation. We believe such training and augmentation methods have potential in benefiting other source separation tasks with limited amount of training data available. In the future, we are interested in extending our experiments to other popular yet less investigated instruments (e.g., guitar and saxophone), and in experimenting with more advanced mixing methods (e.g., applying mixing after the stems are selected, rather than applying mixing to the stems individually beforehand as done here).
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